Kimpan, Warangkhana

Preferred name

Kimpan, Warangkhana

Alternative Name

Kimpan, W.

Main Affiliation

School of Science

warangkhana.ki@kmitl.ac.th

Now showing 1 - 10 of 15

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Heuristic Task Scheduling with Artificial Bee Colony Algorithm for Virtual Machines
(2016-12-28)
Kimpan, Warangkhana
;
Kruekaew, Boonhatai
Cloud computing is one of the Information Technology services which are provided from IT infrastructures to application services. It is the combination of Distributed computing and virtualization technology using virtual machines, an essential component in Cloud computing. Therefore, task scheduling is an important matter to consider for virtual machines to balance load of each machine and to efficiently use the resources in Cloud computing. This paper proposes the use of Heuristic task scheduling with Artificial Bee Colony algorithm for virtual machines in heterogeneous Cloud computing, called HABC. The research aim is to introduce HABC, which is a new task scheduling and load balancing algorithm, for virtual machines in heterogeneous environments to reduce the makespan in the system. In the experiments, CloudSim was simulated to compare various types of the optimization task scheduling in using the virtual machines. The experimental results indicated that using the proposed Artificial Bee Colony algorithm when large job was considered first (HABC-LJF) in virtual machine scheduling, improved the efficiency in task scheduling and load balancing of virtual machines in Cloud computing. In addition, the proposed algorithm can minimize the makespan even if the tasks are increased and the different types of data are distributed.
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Network Architecture of ETAT Education and Training Centers for Automation 4.0
(2022-01-01)
De Marchi, Matteo
;
Jitngernmadan, Prajaks
;
Singsri, Pongpat
;
Putpuek, Narongsak
;
Kumpakeaw, Saman
Automation 4.0 comprises the latest technologies that enhance classic automation to communicate with machines in a connected manner and to enable more flexible and more intelligent automation in smart factories. Industry 4.0 has become a global trend over the past ten years to combine the benefits of automation with the needs for flexible production. This trend has reached, in addition to industrialized countries, also newly industrialized countries like Thailand, with the aim to strengthen the global competitiveness. In the ETAT project, partners from Europe as well as Thailand combine their competences to build up a network of so-called Smart Labs around Bangkok area and thus take an important step towards Automation 4.0 in Thailand. Using Thailand as an example, this work shows how industrially emerging countries can sustainably strengthen their qualification with regard to Automation 4.0 by setting up such networks of education and training centers. In addition to explaining the general architecture of the network, the specific characteristics of all six established ETAT Smart Labs are presented and discussed.
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Define Stance and Swing pattern of gait cycle using motion sensor and K-Mean Clustering
(2022-01-01)
Santikan, Piyapon
;
Tangwongcharoen, Wisan
;
Kimpan, Warangkhana
This research studied walking patterns based on the gait cycle focused on the stance and swing period. We are interested in creating the pattern for representing a normal person and person with a walking disorder by distinguishing patterns. This research uses Razor-IMU to collect all walking data and transfer data from sensors via WIFI which helps gain data to be stable and accurate.After collecting the walking data, we transformed data into linear graphs to reference the gait cycle pattern. Because the graph in linear form can show the movement and distinguish between normal and abnormal people the difference. The aim is to obtain representative data of normal and abnormal people for further analysis. Therefore, the data were then grouped using K-mean Clustering. The data obtained from the clusters were able to distinguish between normal and abnormal walking distances.
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Simulation model of space optimization in containers arranging and sequencing for logistics
(2013-10-25)
Kimpan, Warangkhana
;
Tantawarak, Nake
This paper proposes the application using 3D Bin Packing to optimize the container space for logistics system. The aim is to reduce logistics costs, arrangement time and delivery time. The application can arrange different sizes of cubical boxes into the container bin of trucks, vans, or wagons considering their distributing sequence by using Best-Fit Decreasing algorithm to optimize the space. It also can be used to reduce the cost of product delivering because the container will be divided into many partitions and arranged by using delivery sequence. The different types of boxes and vehicles can be freely adjusted and arranged. The arranged boxes can be viewed in order to check their position. The experimental results of the application are shown in 3D with freely movement view.
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Using mobile application as an instrument for ptosis diagnosis
(2017-07-02)
Kimpan, Warangkhana
;
Sirivimonsattaya, Pongpicha
This article proposes a novel of mobile application to help ophthalmologists diagnose the Ptosis disease. This application applied digital image processing techniques to identify eye parameters: Marginal Reflex Distance-1, Marginal Reflex Distance-2, and L/M Ratio which doctors can use to diagnose patients besides using naked eyes. The application was built for iOS platform in the first version. The inputs of the application are the images of patient's eyes from a mobile device camera or from a mobile device memory. After using digital image processing techniques, at the end of the process, the eye parameters will be displayed on the mobile device screen for the doctors to be used. The experimental results indicated that the proposed mobile application can perform with 86.36% of accuracy in measuring Marginal Reflex Distance-1 value while it can measure Marginal Reflex Distance-2 value with 63.64% of accuracy.
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Thai food recommendation system using hybrid of particle swarm optimization and K-means algorithm
(2021-04-23)
Puraram, Tanakorn
;
Chaovalit, Pimwadee
;
Peethong, Apatha
;
Tiyanunti, Pongsak
;
Charoensiriwath, Supiya
A food recommendation system is an information filtering tool that helps suggest appropriate food menus to users based on their dietary behavior, nutrition, health, or activity. In this paper, a hybrid method of Particle Swarm Optimization (PSO) and K-Means algorithm is proposed to improve the user's dietary behavior clustering and using Principal Component Analysis (PCA) to reduce the data dimension. Moreover, the User-Based Collaborative Filtering technique is used to predict the rating of relevant Thai food menus and recommendation. The experimental result shows the hybrid method improves the clustering performance from 3 models: Hierarchical Clustering, K-Means, and K-Means with PCA, in terms of silhouette coefficient score. In addition, the hybrid method improves the Davies-Bouldin index score by 44%, 19%, and 17% compared to those models, respectively. The rating prediction result shows the hybrid method outperforms the other methods.
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Virtual machine scheduling management on Cloud computing using Artificial Bee Colony
(2014-01-01)
Kruekaew, B.
;
Kimpan, W.
Resource scheduling management design on Cloud computing is an important problem. Scheduling model, cost, quality of service, time, and conditions of the request for access to services are factors to be focused. A good task scheduler should adapt its scheduling strategy to the changing environment and load balancing Cloud task scheduling policy. Therefore, in this paper, Artificial Bee Colony (ABC) is applied to optimize the scheduling of Virtual Machine (VM) on Cloud computing. The main contribution of work is to analyze the difference of VM load balancing algorithm and to reduce the makespan of data processing time. The scheduling strategy was simulated using CloudSim tools. Experimental results indicated that the combination of the proposed ABC algorithm, scheduling based on the size of tasks, and the Longest Job First (LJF) scheduling algorithm performed a good performance scheduling strategy in changing environment and balancing work load which can reduce the makespan of data processing time.
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Water Level Monitoring and Evacuation Guideline Using Ant Colony Optimization on Mobile Application
(2020-08-01)
Kimpan, Warangkhana
;
Kasetvetin, Sirawich
;
Kimpan, Chom
The most natural disasters that have happened in Thailand are storm and flood problems. The people who live near water sources have no warning about the overflowing of water nearby, so they cannot evacuate or get help in time. Thus, there is always a high risk of losing properties or lives. In order to alleviate the losses, this paper proposes water level monitoring on Android application from Internet of Things devices and the guideline for evacuation by applying Ant Colony Optimization which is inspired by the real ant colony. Internet of Things devices are used to monitor the water levels in community for the user who lives near the water sources or near the places which have high risk of flooding. The Hydrostatic level sensors are placed in the water basin near the community to measure the height of the water which can also be observed in real time from mobile application. When the height of the water reaches the critical value that was set in the application, it sends notifications to the user. Moreover, Line bot is used to let the user knows the potential risks from rising water levels. At the critical level, the user needs to evacuate to a safe place located nearby. The application will guide the user to follow the direction to the most safety destination. In case of many people are already evacuated in one place and it reached the maximum amount of limitation, the application will change the recommendation direction to other places nearby using Ant Colony Optimization algorithm for making decisions.
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Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
(2025-01-01)
Teerasarn, Chalanwich
;
Kimpan, Warangkhana
This research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
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Neural network training model for weather forecasting using fireworks algorithm
(2017-02-21)
Suksri, Saktaya
;
Kimpan, Warangkhana
Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by Fireworks Algorithm. Fireworks Algorithm is a recently developed Swarm Intelligence Algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.

Kimpan, Warangkhana

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